Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Cell. 2024 May 9;187(10):2502-2520.e17. doi: 10.1016/j.cell.2024.03.035.
Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.
人体组织本质上是三维（3D）的，传统上通过标准的组织病理学检查作为有限的二维（2D）切片进行检查，由于采样偏差，这些切片可能不足以代表组织。为了全面描述组织形态学，已经开发了 3D 成像方式，但由于复杂的手动评估和缺乏计算平台来从大型高分辨率数据集中提取临床见解，其临床转化受到阻碍。我们提出了 TriPath，这是一个用于处理组织体积的深度学习平台，并能够基于 3D 形态特征来有效预测临床结果。基于开放式光片显微镜或微计算机断层扫描成像的前列腺癌标本对复发风险分层模型进行了训练。通过全面捕获 3D 形态，基于 3D 体积的预后预测比传统的基于 2D 切片的方法具有更好的性能，包括来自六位经认证的泌尿生殖病理学家的临床/组织病理学基线。增加组织体积可以提高预后性能，并减轻采样偏差引起的风险预测变异性，进一步强调了捕获更大程度异质形态的价值。
